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Robot design is a challenging problem involving a balance between the robot’s mechanical design, kinematic structure, and actuation and sensing capabilities. Recent work in computational robot design has focused on mechanical design while assuming that the given actuators are sufficient for the task. At the same time, existing electronics design tools ignore the physical requirements of the actuators and sensors in the circuit. In this paper, we present the first system that closes the loop between the two, incorporating a robot’s mechanical requirements into its circuit design process. We show that the problem can be solved using an iterative search consisting of two parts. First, a dynamic simulator converts the mechanical design and the given task into concrete actuation and sensing requirements. Second, a circuit generator executes a branch-and-bound search to convert the design requirements into a feasible electronic design. The system iterates through both of these steps, a process that is sometimes required since the electronics components add mass that may affect the robot’s design requirements. We demonstrate this approach on two examples — a manipulator and a quadruped — showing in both cases that the system is able to generate a valid electronics design. 

Robot design is a complex cognitive activity that requires the designer to iteratively navigate multiple engineering disciplines and the relations between them. In this paper, we explore how people approach robot design and how trends in design strategy vary with the level of expertise of the designer. Using our interactive Build-a-Bot software tool, we recruited 39 participants from the 2022 IEEE International Conference on Robotics and Automation. These participants varied in age from 19 to 56 years, and had between 0 and 17 years of robotics experience. We tracked the participants’ design decisions over the course of a 15 min. task of designing a ground robot to cross an uneven environment. Our results showed that participants engaged in iterative testing and modification of their designs, but unlike previous studies, there was no statistically significant effect of participant’s expertise on the frequency of iterations. We additionally found that, across levels of expertise, participants were vulnerable to design fixation, in which they latched onto an initial design concept and insufficiently adjusted the design, even when confronted with difficulties developing the concept into a satisfactory solution. The results raise interesting questions for how future engineers can avoid fixation and how design tools can assist in both efficient assessment and optimization of design workflow for complex design tasks. 

We study the effects of injecting human-generated designs into the initial population of an evolutionary robotics experiment, where subsequent population of robots are optimised via a Genetic Algorithm and MAP-Elites. First, human participants interact via a graphical front-end to explore a directly-parameterised legged robot design space and attempt to produce robots via a combination of intuition and trial-and-error that perform well in a range of environments. Environments are generated whose corresponding high-performance robot designs range from intuitive to complex and hard to grasp. Once the human designs have been collected, their impact on the evolutionary process is assessed by replacing a varying number of designs in the initial population with human designs and subsequently running the evolutionary algorithm. Our results suggest that a balance of random and hand-designed initial solutions provides the best performance for the problems considered, and that human designs are most valuable when the problem is intuitive. The influence of human design in an evolutionary algorithm is a highly understudied area, and the insights in this paper may be valuable to the area of AI-based design more generally. 

We report on experiments with a laptop-sized (0.23m, 2.53kg), paper origami robot that exhibits highly dynamic and stable two degree-of-freedom (circular boom) hopping at speeds in excess of 1.5 bl/s (body-lengths per second) at a specific resistance O(1) while achieving aerial phase apex states 25% above the stance height over thousands of cycles. Three conventional brushless DC motors load energy into the folded paper springs through pulley-borne cables whose sudden loss of tension upon touchdown triggers the release of spring potential that accelerates the body back through liftoff to flight with a 20W powerstroke, whereupon the toe angle is adjusted to regulate fore-aft speed. We also demonstrate in the vertical hopping mode the transparency of this actuation scheme by using proprioceptive contact detection with only motor encoder sensing. The combination of actuation and sensing shows potential to lower system complexity for tendon-driven robots.